Meeting Abstract Enhancing Automatic Classification of Hepatocellular Carcinoma Images through Image Masking, Tissue Changes, and Trabecular Features

Background Hepatocellular carcinoma (HCC) is a malignant tumor with hepatocellular differentiation and one of the most common cancers in the world. This type of cancer is often diagnosed when the survival time is measured in months causing high death rates Method We enhanced the classification process presented in [3] by including 11 features of tissue changes (i.e., features related to fatty change, cytoplasm colors, cell clearness index, and stroma) and 10 features of trabecular (e.g., nuclei-cytoplasmic ratio, irregularity of sinusoid, and trabecular arrangements). Furthermore, we apply a mask obtained by the stroma segmentation before calculating the 13 types of nuclear and structural features such that those features are derived from hepatocytes only, thus generating in total 177 features. The experiments were performed on a collection of region-ofinterest (ROI) images extracted from HE stained whole slide images (WSI), consisting of 1054 ROIs of HCC biopsy samples (504 negatives and 550 positives) and 1076 ROIs of HCC surgically resected samples (533 negatives and 543 positives). In the process, we made some combinations on the sets of features and sets of training data from both biopsy and surgery samples. As for the classification, we used 5-fold cross validation support vector machine (SVM) with LibSVM as our library. Results The results of classification experiment are summarized in Conclusion The combination of nuclear, trabecular, and other tissue features enables improved classification rate in HCC detection using SVM. Even though the image characteristics are different in biopsy and surgically resected samples, the same classification system gives good performance in both samples. The HCC classification scheme introduced in this paper is implemented in the prototype "feature measurement software for liver biopsy, " and the probability of HCC is visualized for every ROI in the WSI. It will support pathologists in the HCC diagnosis along with the quantitative measurements of tissue morphology

FLEXPART v10.1 simulation of source contributions to Arctic black carbon

The Arctic environment is undergoing rapid changes such as faster warming than the global average and exceptional melting of glaciers in Greenland. Black carbon (BC) particles, which are a short-lived climate pollutant, are one cause of Arctic warming and glacier melting. However, the sources of BC particles are still uncertain. We simulated the potential emission sensitivity of atmospheric BC present over the Arctic (north of 66∘ N) using the FLEXPART (FLEXible PARTicle) Lagrangian transport model (version 10.1). This version includes a new aerosol wet removal scheme, which better represents particle-scavenging processes than older versions did. Arctic BC at the surface (0–500 m) and high altitudes (4750–5250 m) is sensitive to emissions in high latitude (north of 60∘ N) and mid-latitude (30–60∘ N) regions, respectively. Geospatial sources of Arctic BC were quantified, with a focus on emissions from anthropogenic activities (including domestic biofuel burning) and open biomass burning (including agricultural burning in the open field) in 2010. We found that anthropogenic sources contributed 82 % and 83 % of annual Arctic BC at the surface and high altitudes, respectively. Arctic surface BC comes predominantly from anthropogenic emissions in Russia (56 %), with gas flaring from the Yamalo-Nenets Autonomous Okrug and Komi Republic being the main source (31 % of Arctic surface BC). These results highlight the need for regulations to control BC emissions from gas flaring to mitigate the rapid changes in the Arctic environment. In summer, combined open biomass burning in Siberia, Alaska, and Canada contributes 56 %–85 % (75 % on average) and 40 %–72 % (57 %) of Arctic BC at the surface and high altitudes, respectively. A large fraction (40 %) of BC in the Arctic at high altitudes comes from anthropogenic emissions in East Asia, which suggests that the rapidly growing economies of developing countries could have a non-negligible effect on the Arctic. To our knowledge, this is the first year-round evaluation of Arctic BC sources that has been performed using the new wet deposition scheme in FLEXPART. The study provides a scientific basis for actions to mitigate the rapidly changing Arctic environment

The majority of lipoprotein lipase in plasma is bound to remnant lipoproteins: A new definition of remnant lipoproteins.

BackgroundLipoprotein lipase (LPL) is a multifunctional protein and a key enzyme involved in the regulation of lipoprotein metabolism. We determined the lipoproteins to which LPL is bound in the pre-heparin and post-heparin plasma.MethodsTetrahydrolipstatin (THL), a potent inhibitor of serine lipases, was used to block the lipolytic activity of LPL, thereby preventing changes in the plasma lipoproteins due to ex vivo lipolysis. Gel filtration was performed to obtain the LPL elution profiles in plasma and the isolated remnant lipoproteins (RLP).ResultsWhen ex vivo lipolytic activity was inhibited by THL in the post-heparin plasma, majority of the LPL was found in the VLDL elution range, specifically in the RLP as inactive dimers. However, in the absence of THL, most of the LPL was found in the HDL elution range as active dimers. Furthermore, majority of the LPL in the pre-heparin plasma was found in the RLP as inactive form, with broadly diffused lipoprotein profiles in the presence and absence of THL.ConclusionsIt is suggested that during lipolysis in vivo, the endothelial bound LPL dimers generates RLP, forming circulating RLP-LPL complexes in an inactive form that subsequently binds and initiates receptor-mediated catabolism

Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features

Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis